Dynamic spatial coding in parietal cortex mediates tactile-motor transformation

Movements towards touch on the body require integrating tactile location and body posture information. Tactile processing and movement planning both rely on posterior parietal cortex (PPC) but their interplay is not understood. Here, human participants received tactile stimuli on their crossed and uncrossed feet, dissociating stimulus location relative to anatomy versus external space. Participants pointed to the touch or the equivalent location on the other foot, which dissociates sensory and motor locations. Multi-voxel pattern analysis of concurrently recorded fMRI signals revealed that tactile location was coded anatomically in anterior PPC but spatially in posterior PPC during sensory processing. After movement instructions were specified, PPC exclusively represented the movement goal in space, in regions associated with visuo-motor planning and with regional overlap for sensory, rule-related, and movement coding. Thus, PPC flexibly updates its spatial codes to accommodate rule-based transformation of sensory input to generate movement to environment and own body alike.

MVPA analyses with motion parameters included as nuisance regressors. Our fMRI analyses did not include motion parameters as nuisance regressors in participant GLMs as our unwarping and alignment pre-processing procedure included correction of the fMRI images for movement-related signal intensity changes. To ensure that our results were not affected by any residual movement-related signal changes, we re-ran separate instances of our MVPA analyses that included motion parameters as nuisance regressors in participant GLMs. Results are shown in Fig. S4. In all analyses, locations of clusters are consistent with the analyses without motion parameters. This result suggests that the unwarping procedure had indeed been effective in removing movement-related artifacts.
MVPA analyses in which trials with missing hand movement data were removed. We included trials where an interpretable movement trace could not be extracted from the hand videos in our MVPA analyses as participants showed high performance accuracy for trials in which hand movements could be assessed. To ensure that potential errors in trials with missing hand movement data did not affect our MVPA results, we re-ran separate instances of our MVPA analyses excluding these trials. Note that this strategy results in fewer training data for the MVPA classifiers, especially as some runs of data had to be completely excluded. Results of the new analyses are shown in Fig. S5, and show clusters at thresholds both corrected for multiple comparisons (p < 0.05 FWE corrected) and uncorrected for multiple comparisons (p < 0.01). At the uncorrected threshold, searchlight clusters are located in the same regions as for the MVPA analyses in the main paper, in which we had included trials with missing hand movement data. Thus, results are similar regardless of whether these trials are included or excluded, but power is reduced, as expected, due to the reduction in the total number of trials available for MVPA classification analyses (34% of the total number of trials removed).
fMRI analyses with condition duration modelled as 1 TR rather than the full delay interval. Our GLMs modelled the duration of the sensory processing interval or movement planning interval for each trial (see Fig. 1), as is common in delayed-movement paradigms 1-5 . This strategy aims at modelling sustained neural responses related to sensory processing and movement planning. However, it is also possible that some regions may show only a shorter, more phasic, response following the sensory stimulus or task rule cue. To investigate this possibility, we re-ran separate instances of our MVPA analyses in which the duration of each modelled condition was limited to 1 TR, regardless of the actual length (1-4 TR, see Methods of main paper) of the sensory processing or movement planning interval.
Results of these additional MVPA analyses are shown in Fig. S6. In comparison to results where the full sensory processing or movement planning interval was modelled, decoding of anatomical touch location was found in similar regions, with slightly more lateral clusters in the SPL and more extensive clusters in the insular cortex. External touch location could be decoded bilaterally from regions in the mIPS. Movement goal location could be decoded from a similar fronto-parietal network as in our original analyses, with less extensive clusters in the right hemisphere. Task rule could be decoded from the PPC, as well as additionally from lateral occipital cortex and superior frontal cortex. Note that some additional decoding of task rule could be due to decoding visual differences in the task rule cue or transient coding of the task rule 6 . Such responses are likely prominent during initial decoding but no longer detectable when a longer time interval is analyzed.
Univariate fMRI analyses (Fig. S7) showed higher responses during movement planning to the right versus left side of space in the left SMA, anterior SPL, M1 and the PMd, consistent with the results we obtained in the main paper, when we modelled the full sensory processing or movement planning interval. No regions showed higher responses during movement planning to the left versus right side of space. A small cluster in the left insula showed higher responses to sensory stimuli on the right foot compared to the left foot. Clusters in the left somatosensory and superior temporal cortex showed higher responses to touch on the left vs right side of space. Lastly, clusters in the left posterior PPC, and left superior frontal cortex showed higher responses to the anti-movement task compared to the pro-movement task. None of the other contrasts (left vs right anatomical touch location, right vs left external touch location, pro vs anti task rule) showed any clusters with statistically significant differences in responses. Supplementary Fig. 1. Full design scheme of MVPA decoding analyses. Within-interval pattern classification refers to analyses in which both training and test data stemmed from the same trial interval, which was either Supplementary Fig. 2. Full design scheme of the task rule decoding analysis to distinguish patterns of responses evoked by pro-vs. anti-pointing movements during the movement planning interval. fMRI data from the touch localization interval (blue boxes, left) or the movement planning interval (purple boxes, right). Cross-interval classification refers to analyses in which training data from the touch localization interval were used to predict the classification labels of test data from the movement planning interval. A Pooling of conditions to decode anatomical touch location (left vs. right foot). B Pooling of conditions to decode external touch location, i.e., spatial side of touch. Note, that foot crossing dissociates anatomical from external location (denoted by the lightning-bolt in circled vs. squared boxes on the right side of the figure). C Pooling of conditions to decode the location of the movement goal. The combination of foot posture, stimulation side, and pro/anti-pointing dissociates sensory and movement coding. MVPA results for decoding movement goal location (pointing movement to the foot on the right or left side of space) during the movement planning interval. D MVPA results for decoding task rule (pro-vs. anti-pointing movement) during the movement planning interval. Results show clusters with significantly higher group-level activation (one-sided t-test), corrected for multiple comparisons using a cluster-based permutation test (FWE, p<.05). LH, left hemisphere; RH, right hemisphere. Supplementary Fig. 7. Results of univariate analyses in which the modelled duration was reduced to 1 TR for all trials independent of the actual duration of the touch localization or movement planning interval. A Regions showing higher BOLD activation during sensory processing of stimuli with anatomical location on the right foot as compared to the left foot. B Regions showing higher BOLD activation during sensory processing of stimuli with external location on the left side of space as compared to the right side of space. C Regions showing higher BOLD activation during movement planning to targets on the right as compared to targets on the left. D Regions showing higher BOLD activation during anti-movement planning as compared to pro-movement planning. Results show clusters with significantly higher group-level activation (one-sided t-test), corrected for multiple comparisons using a cluster-based permutation test (FWE, p<.05). LH, left hemisphere; RH, right hemisphere.